Digital Twin – Transforming IoT Industry in 2021

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IoT stands for the Internet of Things. IoT development companies share related information like the latest IoT trends, digital twin technology applications, IoT roadmap, etc.

With the rise in applications of the Internet of Things (IoT), Digital Twin adoption has caught up across industries. Digital Twins is leading the way in transforming IoT. The term came into existence when Michael Grieves, then computer engineer at the University of Michigan, wrote about it in 2002 and was named one of Gartner’s Top 10 Strategic Technology Trends for 2018.

In the current scenario, processes and machines are complex and the risk of failure or disruption is high and costly. Using IoT sensors, a digital twin captures information from a real-world proxy and then simulates the physical object in real-time. Thus, offering insights into asset behavior – likely issues and performance. 

What is Digital Twin?

It is a fully working digital version of a system/product/asset. Digital Twin is a digital replica of any physical product that describes it from the micro atomic level to the macro geometrical level. Any information that could be obtained from inspecting a physically manufactured product can be obtained from its Digital Twin.

digital twin in iot

  • Digital Twin behaves the same as the Real System would  
  • It allows for virtual testing, before applying it in the real world
  • It predicts and shows what will happen
  • Virtual damage/errors instead of real damage/errors

How does Digital Twin work?

It acts as a bridge between the physical and digital worlds.

First, smart components are connected to a physical item that uses sensors to collect real-time data on status, working conditions, location, etc. These components are connected to a cloud-based system that analyses all the data captured by the sensors. 

Read More :Artificial Intelligence|Machine Learning and Their Smart Applications

The data is monitored and tracked regularly to identify patterns and capture the working of the physical item. The analyses are then compared to business expectations and expected outcomes to align decision-making accordingly.

Advantages of Digital Twin Technology

There are many companies using digital twins. It is not new; rather, brings the focus on data analytics and management. Digital twin solutions can be implemented through digital twin product development. Some of the advantages of Digital Twin are as below:

  • Improved OEE (Overall Equipment Effectiveness) through reduced downtime and improved performance.
  • Better productivity.
  • Lower maintenance costs through predictive maintenance (notifying issues before breakdown).
  • Insight into product performance and working pattern.

Digital Twin Examples

From manufacturing and healthcare to retail and maintenance, IoT application is seen in every industry.

Digital Twin implementation examples with reference to some of the industries are as below:

  • Manufacturing

1.Product development

2.Operational improvements

3.Predictive maintenance

  • Supply Chain

1.Predict the performance of packaging materials

2.Enhance shipment protection

3.Track location & condition of goods in real-time

4.Warehouse/Inventory Management: Ensure the right products in the right place at the right time

Read More :IOT Advancement in Supply Chain Management

  • Healthcare

1.Improving operational efficiency of healthcare operations

2.Implementing personalized care

  • Retail

1.Customer modeling & simulations

Digital Twin And AI

Digital twins build on AI, IoT, and software analytics to create a digital replica of a real-life asset that interactively updates data/information with their physical counterparts. 

Advantages of Digital Twin Technology

Artificial Intelligence (AI), Machine Learning (ML), and other advanced technologies use digital twin models that share information update with respect to the asset. The use case of AI and digital twin technology in monitoring asset performance involves data analysis of the machine-related attributes in real-time. 

Read More :Digital Twin Technology in Manufacturing Industry

Through AI in Digital Twin Technology, businesses can build a model that captures the elements of assets and capture the images while in action and use the data and images for effective decision making. This information on assets helps AI to make complex decisions. 

Conclusion

The technology-based digital transformation is scaling its scope with each passing day across industries. Bringing together hardware, software, and data, Digital Twin implementation drives the optimum design and operation of a product or service. Digital Twins represent a fundamental shift in the scope and scale of engineering. 

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Manufacturing companies are already using digital twins to augment industrial processes and focus on approaches to reduce costs, monitor assets, streamline maintenance, and lower downtime. Digital twin cost varies on basis of the requirement and the digital twin platform being used.

In the coming years, digital twins will likely see more and more applications across many industries. This means that in the future development of IoT, almost every manufactured product could have its own digital twin, assuming it is generating data that can be analyzed.  

FAQ's

Industry 4.0 is also known as the ‘fourth industrial revolution. It is a current trend in which manufacturing is increasingly automated and reliant on data, often referred to as ‘Big Data, via the IoT and cloud computing. ‘Smart Factory’ is a concept implemented through Industry 4.0 technologies. Digital twin fits into this model as a virtual proxy of the asset.

Augmented reality is the integration of digital information with the user’s environment in real-time. In AR, the digital twin must be connected with the product’s movement and location that can be used in applications for Aftermarket services (AMC, product maintenance, etc.). This is the future of digital twin.

IoT digital twin is a virtual representation of a physical asset, a machine, or a device. It digitally represents the lifecycle of the asset including operational data, working trends, etc.

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